An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

Confronting the data deluge: How artificial intelligence can be used in the study of plant stress

The advent of the genomics era enabled the generation of high-throughput data and computational methods that serve as powerful hypothesis-generating tools to understand the genomic and gene functional basis of plant stress resilience. The proliferation of experimental and analytical methods used in biology has resulted in a situation where plentiful data exists, but the volume and heterogeneity of this data has made analysis a significant challenge. Current advanced deep-learning models have displayed an unprecedented level of comprehension and problem-solving ability, and have been used to predict gene structure, function and expression based on DNA or protein sequence, and prominently also their use in high-throughput phenomics in agriculture. However, the application of deep-learning models to understand gene regulatory and signalling behaviour is still in its infancy. We discuss in this review the availability of data resources and bioinformatic tools, and several applications of these advanced ML/AI models in the context of plant stress response, and demonstrate the use of a publicly available LLM (ChatGPT) to derive a knowledge graph of various experimental and computational methods used in the study of plant stress. We hope this will stimulate further interest in collaboration between computer scientists, computational biologists and plant scientists to distil the deluge of genomic, transcriptomic, proteomic, metabolomic and phenomic data into meaningful knowledge that can be used for the benefit of humanity.

AutoXAI4Omics: an automated explainable AI tool for omics and tabular data

Machine learning (ML) methods offer opportunities for gaining insights into the intricate workings of complex biological systems, and their applications are increasingly prominent in the analysis of omics data to facilitate tasks, such as the identification of novel biomarkers and predictive modeling of phenotypes. For scientists and domain experts, leveraging user-friendly ML pipelines can be incredibly valuable, enabling them to run sophisticated, robust, and interpretable models without requiring in-depth expertise in coding or algorithmic optimization. By streamlining the process of model development and training, researchers can devote their time and energies to the critical tasks of biological interpretation and validation, thereby maximizing the scientific impact of ML-driven insights. Here, we present an entirely automated open-source explainable AI tool, AutoXAI4Omics, that performs classification and regression tasks from omics and tabular numerical data. AutoXAI4Omics accelerates scientific discovery by automating processes and decisions made by AI experts, e.g. selection of the best feature set, hyper-tuning of different ML algorithms and selection of the best ML model for a specific task and dataset. Prior to ML analysis AutoXAI4Omics incorporates feature filtering options that are tailored to specific omic data types. Moreover, the insights into the predictions that are provided by the tool through explainability analysis highlight associations between omic feature values and the targets under investigation, e.g. predicted phenotypes, facilitating the identification of novel actionable insights. AutoXAI4Omics is available at: https://github.com/IBM/AutoXAI4Omics.

Advances in understanding plant-pathogen interactions: insights from tomato as a model system

The impact of plant diseases coupled with climate change on agriculture worldwide cannot be overemphasized from negative effects on crop yield as well as economy to food insecurity. The model plants are essential for understanding the intricacies of plant-pathogen interactions. One of such plants is the tomato (Solanum lycopersicum L.). Researchers hope to increase tomato productivity and boost its resilience to pathogen attacks by utilizing OMICS and biotechnological methods. With an emphasis on tomato viral infections, this review summarizes significant discoveries and developments from earlier research. The analysis elucidates ongoing efforts to advance plant pathology by exploring the implications for sustainability and tomato production.

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

Beyond the Horizon: A Comprehensive Review of Contemporary Strategies in Sepsis Management Encompassing Predictors, Diagnostic Tools, and Therapeutic Advances

This comprehensive review offers a detailed exposition of contemporary strategies in sepsis management, encompassing predictors, diagnostic tools, and therapeutic advances. The analysis elucidates the dynamic nature of sepsis, emphasizing the crucial role of early detection and intervention. The multifaceted strategies advocate for a holistic and personalized approach to sepsis care from traditional clinical methodologies to cutting-edge technologies. The implications for clinical practice underscore clinicians' need to adapt to evolving definitions, integrate advanced diagnostic tools, and embrace precision medicine. Integrating artificial intelligence and telemedicine necessitates a commitment to training and optimization. Judicious antibiotic use and recognition of global health disparities emphasize the importance of a collaborative, global effort in sepsis care. Looking ahead, recommendations for future research underscore priorities such as longitudinal studies on biomarkers, precision medicine trials, implementation science in technology, global health interventions, and innovative antibiotic stewardship strategies. These research priorities aim to contribute to transformative advancements in sepsis management, ultimately enhancing patient outcomes and reducing the global impact of this critical syndrome.

Bioprospecting of Aspergillus sp. as a promising repository for anti-cancer agents: a comprehensive bibliometric investigation

Cancer remains a significant global health challenge, claiming nearly 10 million lives in 2020 according to the World Health Organization. In the quest for novel treatments, fungi, especially Aspergillus species, have emerged as a valuable source of bioactive compounds with promising anticancer properties. This study conducts a comprehensive bibliometric analysis to map the research landscape of Aspergillus in oncology, examining publications from 1982 to the present. We observed a marked increase in research activity starting in 2000, with a notable peak from 2005 onwards. The analysis identifies key contributors, including Mohamed GG, who has authored 15 papers with 322 citations, and El-Sayed Asa, with 14 papers and 264 citations. Leading countries in this research field include India, Egypt, and China, with King Saud University and Cairo University as the leading institutions. Prominent research themes identified are "endophyte," "green synthesis," "antimicrobial," "anti-cancer," and "biological activities," indicating a shift towards environmentally sustainable drug development. Our findings highlight the considerable potential of Aspergillus for developing new anticancer therapies and underscore the necessity for further research to harness these natural compounds for clinical use.

The role of priming and memory in rice environmental stress adaptation: Current knowledge and perspectives

Plant responses to abiotic stresses are dynamic, following the unpredictable changes of physical environmental parameters such as temperature, water and nutrients. Physiological and phenotypical responses to stress are intercalated by periods of recovery. An earlier stress can be remembered as 'stress memory' to mount a response within a generation or transgenerationally. The 'stress priming' phenomenon allows plants to respond quickly and more robustly to stressors to increase survival, and therefore has significant implications for agriculture. Although evidence for stress memory in various plant species is accumulating, understanding of the mechanisms implicated, especially for crops of agricultural interest, is in its infancy. Rice is a major food crop which is susceptible to abiotic stresses causing constraints on its cultivation and yield globally. Advancing the understanding of the stress response network will thus have a significant impact on rice sustainable production and global food security in the face of climate change. Therefore, this review highlights the effects of priming on rice abiotic stress tolerance and focuses on specific aspects of stress memory, its perpetuation and its regulation at epigenetic, transcriptional, metabolic as well as physiological levels. The open questions and future directions in this exciting research field are also laid out.